![](images/batch-3.png)
	
|  | Element | Description |
|--|---------|-------------|
| ![](images/icon-1.png) | Include input features | Writes input features to the prediction results file alongside predictions. To add specific features, enable the **Include input features** toggle, select **Specific features**, and type feature names to filter for and then select features. To include every feature from the dataset, select **All features**. You can only append a feature (column) present in the original dataset, although the feature does not have to have been part of the feature list used to build the model. Derived features are *not* included. |
| ![](images/icon-2.png) | Include Prediction Explanations | Adds columns for [Prediction Explanations](pred-explain/index) to your prediction output.<ul><li>**Number of explanations**: Enter the maximum number of explanations you want to request from the deployed model. You can request **100** explanations per prediction request.</li><li>**Low prediction threshold**: Enable and define this threshold to provide prediction explanations for _any_ values _below_ the set threshold value.</li><li>**High prediction threshold**: Enable and define this threshold to provide prediction explanations for _any_ values _above_ the set threshold value.</li><li>**Number of ngram explanations**: Enable and define the maximum number of text [ngram](glossary/index#n-gram) explanations to return per row of the dataset. The default (and recommended) setting is **all** (no limit).</li></ul> If you can't enable Prediction Explanations, see [Why can't I enable Prediction Explanations?](#include-prediction-explanations). |
| ![](images/icon-3.png) | Include prediction outlier warning | Includes warnings for [outlier prediction values](humility-settings#prediction-warnings) (only available for regression model deployments).|
| ![](images/icon-4.png) | Track data drift, accuracy, and fairness for predictions | Tracks [data drift](data-drift), [accuracy](deploy-accuracy), and [fairness](mlops-fairness) (if enabled for the deployment). |
| ![](images/icon-5.png) | Chunk size | Adjusts the chunk size selection strategy. By default, DataRobot automatically calculates the chunk size; only modify this setting if advised by your DataRobot representative. For more information, see [What is chunk size?](#what-is-chunk-size) |
| ![](images/icon-6.png) | Concurrent prediction requests | Limits the number of concurrent prediction requests. By default, prediction jobs utilize all available prediction server cores. To reserve bandwidth for real-time predictions, set a cap for the maximum number of concurrent prediction requests. |
| ![](images/icon-7.png) | Include prediction status | Adds a column containing the status of the prediction. |
| ![](images/icon-8.png) | Use default prediction instance | Lets you change the [prediction instance](pred-env#prediction-environments). Turn the toggle off to select a prediction instance. |

??? faq "Why can't I enable Prediction Explanations?"
    If you can't <span id="include-prediction-explanations">**Include Prediction Explanations**</span>, it is likely because:

    * The model's validation partition doesn't contain the required number of rows.

    * For a Combined Model, at least one segment champion validation partition doesn't contain the required number of rows. To enable Prediction Explanations, manually replace retrained champions before creating a model package or deployment.

??? faq "What is chunk size?"
    The batch prediction process <span id="what-is-chunk-size">chunks</span> your data into smaller pieces and scores those pieces one by one, allowing DataRobot to score large batches. The **Chunk size** setting determines the strategy DataRobot uses to chunk your data. DataRobot recommends the default setting of **Auto** chunking, as it performs the best overall; however, other options are available:
    
    * **Fixed**: DataRobot identifies an initial, effective chunk size and continues to use it for the rest of the model scoring process.

    * **Dynamic**: DataRobot increases the chunk size while model scoring speed is acceptable and decreases the chunk size if the scoring speed falls.

    * **Custom**: A data scientist sets the chunk size, and DataRobot continues to use it for the rest of the model scoring process.